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Blast‐induced traumatic brain injury (TBI) is a signature injury of the current military conflicts. Among the different types of TBI, diffuse axonal injury (DAI) plays an important role since it can lead to devastating effects in the inflicted military personnel. To better understand the potential causes associated with DAI, this paper aims to investigate a transient non‐linear dynamics finite element simulation of the response of the brain white matter to shock loading.

Brain white matter is considered to be a heterogeneous material consisting of fiber‐like axons and a structure‐less extracellular matrix (ECM). The brain white matter microstructure in the investigated corpus callosum region of the brain is idealized using a regular hexagonal arrangement of aligned equal‐size axons. Deviatoric stress response of the axon and the ECM is modeled using a linear isotropic viscoelastic formulation while the hydrostatic stress response is modeled using a shock‐type equation of state. To account for the stochastic character of the brain white matter microstructure and shock loading, a parametric study is carried out involving a factorial variation of the key microstructural and waveform parameters.

The results obtained show that the extent of axon undulations and the strength of axon/ECM bonding profoundly affect the spatial distribution and magnitude of the axonal axial normal and shear stresses (the stresses which can cause diffuse axonal injury).

The present approach enables a more accurate determination of the mechanical behavior of brain white matter when subjected to a shock.

Transient calculation of currents in brain tissue induced during a transcranial magnetic stimulation treatment.

Because of the short pulses used in this technique a time‐harmonic approximation is no longer valid, and transient effects have to be considered. We have performed a Fourier analysis of the induced currents calculated in a high‐resolution model of the brain using the extended scalar potential finite differences (Ex‐SPFD) approach.

The peak induced currents in the transient development of the pulse are higher by a factor of approximately seven than the time harmonic solutions at the fundamental frequency. Furthermore, an analysis of the impact of the conductivity dispersion revealed an increase in the peak induced currents by 17.3 percent for white matter and by 20.8 percent for gray matter.

Using the numerically efficient Ex‐SPFD approach, along with a high performance cluster, the current densities inside the brain can be calculated incorporating more details than previous calculations of this type.

This chapter interrogates notions of the child and her brain as configured in the laboratory of pediatric neuroscientists, and by parents (overwhelmingly: mothers) of children classified for special education services on the basis of their varied learning capacities and incapacities. Data are drawn from my current New York-based study in a laboratory conducting fMRI research on resting-state differences amongst controls and children variously diagnosed with attention deficit hyper-activity disorder (ADHD), learning disabilities, autism and Tourette syndrome. Parents of children with those same diagnoses struggle with the strengths as well as the school-based weaknesses of their children, and in interviews they picture their children's brains quite differently than do the scientists. Young adult activists who grew up with the diagnoses of ADHD and learning disabilities appropriate lab-based descriptions of neurological difference to their own purposes, claiming a positive identity for themselves. At stake in the space between these diverse perspectives on childhood difference is the future of human developmental variability as it comes under biomedical research and regulation.

Neuroeconomics and neurofinance are emerging as intriguing fields of research, despite sharing ambiguity with the concepts of neuroscience. The relationship among the concepts of economics, finance and neuroscience is not explicitly defined in the past literature, which distorts the use of neuroeconomics and neurofinance approaches in real-world practice for financial decision-making. The purpose of this paper is to consolidate the literature in the field of neuroeconomics and neurofinance to set up the research agenda for the upcoming scholarship in the field.

The purpose of this paper is to consolidates the extant literature in the fields of neuroeconomics and neurofinance by conducting an extensive systematic literature review to investigate the current state and define the relationship between economics, finance and neuroscience.

This paper identifies and explains the explicit relationship between different sub-fields of neuroscience with neuroeconomics and neurofinance and providing instances for future research studies.

The exclusive and extensive literature survey in the form of systematic literature review is undertaken for understanding the fields of neuroeconomics and neurofinance and is the key highlight of this paper. Another, interesting fact lies with matching the literature in neuroeconomics and neurofinance with further sub-fields of neuroscience such as neurophysiology, neuroanatomy, molecular neuroscience and cognitive neuroscience.

Currently, there are some finite element head models developed by research groups all around the world. Nevertheless, the majority are not geometrically accurate. One of the problems is the brain geometry, which usually resembles a sphere. This may raise problems when reconstructing any event that involves brain kinematics, such as accidents, affecting the correct evaluation of resulting injuries. Thus, the purpose of this study is to develop a new finite element head model more accurate than the existing ones.

In this work, a new and geometrically detailed finite element brain model is proposed. Special attention was given to sulci and gyri modelling, making this model more geometrically accurate than currently available ones. In addition, these brain features are important to predict specific injuries such as brain contusions, which usually involve the crowns of gyri.

The model was validated against experimental data from impact tests on cadavers, comparing the intracranial pressure at frontal, parietal, occipital and posterior fossa regions.

As this model is validated, it can be now used in accident reconstruction and injury evaluation and even as a design tool for protective head gear.

The purpose of this paper is to optimize the design of the advanced combat helmet (ACH) currently in use, by its designers in order to attain maximum protection against ballistic impacts (fragments, shrapnel, etc.) and hard-surface/head collisions. Since traumatic brain injury experienced by a significant fraction of the soldiers returning from the recent conflicts is associated with their exposure to blast, the ACH should be redesigned in order to provide the necessary level of protection against blast loads. In the present work, augmentations of the ACH for improved blast protections are considered. These augmentations include the use of a polyurea (a nano-segregated elastomeric copolymer)-based ACH external coating/internal lining.

To demonstrate the efficacy of this approach, instrumented (unprotected, standard-ACH-protected, and augmented-ACH-protected) head-mannequin blast experiments are carried out. These experimental efforts are complemented with the appropriate combined Eulerian/Lagrangian transient non-linear dynamics computational fluid/solid interaction analysis.

The results obtained indicated that: when the extent of peak over-pressure reduction is used as a measure of the blast-mitigation effectiveness, polyurea-based augmentations do not noticeably improve, and sometimes slightly worsen, the performance of the standard ACH; when the extent of specific impulse reduction is used as a measure of the blast-mitigation effectiveness, application of the polyurea external coating to the standard ACH improves the blast-mitigation effectiveness of the helmet, particularly at shorter values of the charge-detonation standoff distance (SOD). At longer SODs, the effects of the polyurea-based ACH augmentations on the blast-mitigation efficacy of the standard ACH are inconclusive; and the use of the standard ACH significantly lowers the accelerations experienced by the skull and the intracranial matter. As far as the polyurea-based augmentations are concerned, only the internal lining at shorter SODs appears to yield additional reductions in the head accelerations.

To the authors’ knowledge, the present work contains the first report of a combined experimental/computational study addressing the problem of blast-mitigation by polyurea-based augmentation of ACH.

The design of the Advanced Combat Helmet (ACH) currently in use was optimized by its designers in order to attain maximum protection against ballistic impacts (fragments, shrapnel, etc.) and hard-surface/head collisions. Since traumatic brain injury experienced by a significant fraction of the soldiers returning from the recent conflicts is associated with their exposure to blast, the ACH should be redesigned in order to provide the necessary level of protection against blast loads. The paper aims to discuss this issue.

In the present work, an augmentation of the ACH for improved blast protection is considered. This augmentation includes the use of a polyurea (a nano-segregated elastomeric copolymer) based ACH external coating. To demonstrate the efficacy of this approach, blast experiments are carried out on instrumented head-mannequins (without protection, protected using a standard ACH, and protected using an ACH augmented by a polyurea explosive-resistant coating (ERC)). These experimental efforts are complemented with the appropriate combined Eulerian/Lagrangian transient non-linear dynamics computational fluid/solid interaction finite-element analysis.

The results obtained clearly demonstrated that the use of an ERC on an ACH affects (generally in a beneficial way) head-mannequin dynamic loading and kinematic response as quantified by the intracranial pressure, impulse, acceleration and jolt.

To the authors’ knowledge, the present work is the first reported combined experimental/computational study of the blast-protection efficacy and the mild traumatic brain-injury mitigation potential of polyurea when used as an external coating on a helmet.

While traumatic brain injury (TBI) became a special education category within the Individuals with Disabilities Education Act (IDEA) in 1990, societies have dealt with TBI far back in history. According to Granacher (2007), there have been writings about the examination of skulls from battlefields in which a hole was drilled into the skull using a trepanning tool apparently to provide some physical relief for the injured soldier. Interestingly, Levin, Benton, and Grossman (1982) stated that this tool continued to be part of Medieval and Renaissance surgeons' practice. At that time, the surgeons believed that trepanation was a vital procedure to improve the brain pulsations and hence the overall well-being of the person with a TBI; however, the medical effectiveness of this procedure did not materialize and it was replaced by brain surgery in the 20th century (Levin et al., 1982).

Full text of a document is a rich source of information that can be used to provide meaningful topics. The purpose of this paper is to demonstrate how to use citation context (CC) in the full text to identify the cited topics and citing topics efficiently and effectively by employing automatic text analysis algorithms.

The authors present two novel topic models, Citation-Context-LDA (CC-LDA) and Citation-Context-Reference-LDA (CCRef-LDA). CC is leveraged to extract the citing text from the full text, which makes it possible to discover topics with accuracy. CC-LDA incorporates CC, citing text, and their latent relationship, while CCRef-LDA incorporates CC, citing text, their latent relationship and reference information in CC. Collapsed Gibbs sampling is used to achieve an approximate estimation. The capacity of CC-LDA to simultaneously learn cited topics and citing topics together with their links is investigated. Moreover, a topic influence measure method based on CC-LDA is proposed and applied to create links between the two-level topics. In addition, the capacity of CCRef-LDA to discover topic influential references is also investigated.

The results indicate CC-LDA and CCRef-LDA achieve improved or comparable performance in terms of both perplexity and symmetric Kullback–Leibler (sKL) divergence. Moreover, CC-LDA is effective in discovering the cited topics and citing topics with topic influence, and CCRef-LDA is able to find the cited topic influential references.

The automatic method provides novel knowledge for cited topics and citing topics discovery. Topic influence learnt by our model can link two-level topics and create a semantic topic network. The method can also use topic specificity as a feature to rank references.